惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

B
Blog RSS Feed
Jina AI
Jina AI
雷峰网
雷峰网
Blog — PlanetScale
Blog — PlanetScale
Hugging Face - Blog
Hugging Face - Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Last Week in AI
Last Week in AI
博客园 - 司徒正美
罗磊的独立博客
J
Java Code Geeks
Engineering at Meta
Engineering at Meta
WordPress大学
WordPress大学
Vercel News
Vercel News
A
About on SuperTechFans
I
InfoQ
D
DataBreaches.Net
爱范儿
爱范儿
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
U
Unit 42
aimingoo的专栏
aimingoo的专栏
宝玉的分享
宝玉的分享
P
Proofpoint News Feed
Microsoft Azure Blog
Microsoft Azure Blog
美团技术团队

cs.IT updates on arXiv.org

Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Joint remote state preparation (JRSP) of two-qubit equato...
Adenike Grace Adepoju, Babatunde James Falaye, Guo-Hua Sun, Osca · 2016-12-08 · via cs.IT updates on arXiv.org

This letter reports the influence of noisy channels on JRSP of two-qubit equatorial state. We present a scheme for JRSP of two-qubit equatorial state. We employ two tripartite Greenberger-Horne-Zeilinger (GHZ) entangled states as the quantum channel linking the parties. We find the success probability to be $1/4$. However, this probability can be ameliorated to $3/4$ if the state preparers assist by transmitting individual partial information through classical channel to the receiver non-contemporaneously. Afterward, we investigate the effects of five quantum noises: the bit-flip noise, bit-phase flip noise, amplitude-damping noise, phase-damping noise and depolarizing noise on the JRSP process. We obtain the analytical derivation of the fidelities corresponding to each quantum noisy channel, which is a measure of information loss as the qubits are being distributed in these quantum channels. We find that the system loses some of its properties as a consequence of unwanted interactions with environment. For instance, within the domain $0<λ<0.65$, the information lost via transmission of qubits in amplitude channel is most minimal, while for $0.65<λ\leq1$, the information lost in phase flip channel becomes the most minimal. Also, for any given $λ$, the information transmitted through depolarizing channel has the least chance of success.